This is the experimental assignment of my course "Machine Learning and Data Mining", which requires completing the training, testing and evaluation of the linear regression model for house price prediction based on the California Housing Prices dataset

House Price Prediction of the California Dataset from Scikit Learn

ML based WebApp for california House-Price Prediction deployed over Railways.app . Scroll down to see screenshots of the WebApp

Implemented Spark machine learning Pipeline on AWS EMR for Collaborative Filtering to recommend users which online educational course they should take based on their viewing history. Target audience found using K-Means clustering over 2 billion data rows. • Using Kafka & Spark Structured Streaming simulated the above models as real time events with a window size of 2 minutes. • House price prediction for California residents based on Kaggle’s 2014/15 dataset using Linear Regression. Narrowed down the customers who were likely to purchase using Logistic Regression & Decision Tree Classifier along with Random Forests to choose the best performing model.

House price prediction system of boston California housing

Interactive Streamlit app predicting California house prices using the 1990 U.S. Census dataset. Features real-time price predictions with Linear Regression, based on median income, house age, and average rooms, plus insightful visualizations of key housing data.

The development of a housing price prediction model can assist a house seller or a real estate agent to make better-informed decisions based on house price valuation. In this project, I used machine learning (ML) predictive modeling and classification algorithms to predict the values of properties in California.

In my project there is an application with which you can work with a database of apartments in California, as well as view statistical reports on dataset. Also, the project implemented a prediction of the location of the house and its prices using machine learning algorithms

Using Multiple Model to predict prices using Python

Prediction of california house pricing

Prediction of House Prices in California

Prediction Prices of Houses in different regions of California

Prediction of california house prices by XGBRegressor

House price prediction of California using XGBoost regressor

Prediction of house price in California using ML.

this repository contains the prediction model of houses prices in California

Prediction the price of california's houses based on different features.

PREDICTION OF HOUSE PRICES USING KAGGLE'S CALIFORNIA DATASET

Prediction of house prices in California based on historical data

Prediction of house prices in California using Python and sklearn

It is a regresstion model on the price prediction of houses in california

Prediction of the median house prices in california's districts using california housing dataset

Prediction of house prices in California in 1990s. Data from https://www.kaggle.com/camnugent/california-housing-prices

Analysis of California median housing. Prediction of median house price through multiple factors.

I have used a dataset of house prices in California from Kaggle. Built a model for the prediction of house prices.

Prediction of house price in california using linear, ridge, and lasso regression and comparision between them

Usage and implementation of a machine learning algorithm to allow for house price predictions in california.

This is a housepricing prediction model built using the California House Price dataset to be able to predict the price of future houses in the district.

California Housing Price Prediction : The project aims at building a model of housing prices to predict median house values in California using the provided dataset.

Prediction house prices of California state university using Random forest random forest regressor and dataset from scikit learn